{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2020, NVIDIA CORPORATION.\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, time\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np, gc\n",
    "from datetime import datetime\n",
    "import joblib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "import cudf, cupy, time\n",
    "cudf.__version__\n",
    "\n",
    "startNB = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit, njit, prange\n",
    "from sklearn.metrics import precision_recall_curve, auc, log_loss\n",
    "\n",
    "def compute_prauc(gt, pred, nafill=True):\n",
    "    if nafill:\n",
    "        pred[ np.isnan(pred) ] = np.nanmean( pred )\n",
    "    prec, recall, thresh = precision_recall_curve(gt, pred)\n",
    "    prauc = auc(recall, prec)\n",
    "    return prauc\n",
    "\n",
    "@jit\n",
    "def fast_auc(y_true, y_prob):\n",
    "    y_true = np.asarray(y_true)\n",
    "    y_true = y_true[np.argsort(y_prob)]\n",
    "    nfalse = 0\n",
    "    auc = 0\n",
    "    n = len(y_true)\n",
    "    for i in range(n):\n",
    "        y_i = y_true[i]\n",
    "        nfalse += (1 - y_i)\n",
    "        auc += y_i * nfalse\n",
    "    auc /= (nfalse * (n - nfalse))\n",
    "    return auc\n",
    "\n",
    "@njit\n",
    "def numba_log_loss(y,x):\n",
    "    n = x.shape[0]\n",
    "    ll = 0.\n",
    "    for i in prange(n):\n",
    "        if y[i]<=0.:\n",
    "            ll += np.log(1-x[i] + 1e-15 )\n",
    "        else:\n",
    "            ll += np.log(x[i] + 1e-15)\n",
    "    return -ll / n\n",
    "\n",
    "def compute_rce(gt , pred, nafill=True, verbose=0):\n",
    "    if nafill:\n",
    "        pred[ np.isnan(pred) ] = np.nanmean( pred )\n",
    "        \n",
    "    cross_entropy = numba_log_loss( gt, pred  )\n",
    "    \n",
    "    yt = np.mean(gt>0)     \n",
    "    strawman_cross_entropy = -(yt*np.log(yt) + (1 - yt)*np.log(1 - yt))\n",
    "    \n",
    "    if verbose:\n",
    "        print( \"logloss: {0:.5f} / {1:.5f} = {2:.5f}\".format(cross_entropy, strawman_cross_entropy, cross_entropy/strawman_cross_entropy))\n",
    "        print( 'mean:    {0:.5f} / {1:.5f}'.format( np.nanmean( pred ) , yt  ) )\n",
    "    \n",
    "    return (1.0 - cross_entropy/strawman_cross_entropy)*100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_memory( df ):\n",
    "    features = df.columns\n",
    "    for i in range( df.shape[1] ):\n",
    "        if df.dtypes[i] == 'uint8':\n",
    "            df[features[i]] = df[features[i]].astype( np.int8 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'bool':\n",
    "            df[features[i]] = df[features[i]].astype( np.int8 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'uint32':\n",
    "            df[features[i]] = df[features[i]].astype( np.int32 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'int64':\n",
    "            df[features[i]] = df[features[i]].astype( np.int32 )\n",
    "            gc.collect()\n",
    "        elif df.dtypes[i] == 'float64':\n",
    "            df[features[i]] = df[features[i]].astype( np.float32 )\n",
    "            gc.collect()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET_id = 1\n",
    "\n",
    "TARGETS = ['reply', 'retweet', 'retweet_comment', 'like']\n",
    "\n",
    "TARGET = TARGETS[TARGET_id]\n",
    "\n",
    "train = pd.read_parquet( 'data/train-final-te-'+TARGET+'-1.parquet' )\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['a_follower_count']  = train.groupby('a_user_id')['a_follower_count' ].transform('last');_ = gc.collect()\n",
    "train['a_following_count'] = train.groupby('a_user_id')['a_following_count'].transform('last');_ = gc.collect()\n",
    "train['b_follower_count']  = train.groupby('b_user_id')['a_follower_count' ].transform('last');_ = gc.collect()\n",
    "train['b_following_count'] = train.groupby('b_user_id')['a_following_count'].transform('last');_ = gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
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       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_id_retweet</th>\n",
       "      <th>TE_b_user_id_retweet</th>\n",
       "      <th>TE_tw_hash_retweet</th>\n",
       "      <th>TE_tw_freq_hash_retweet</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087427</td>\n",
       "      <td>0.065124</td>\n",
       "      <td>0.068161</td>\n",
       "      <td>0.065125</td>\n",
       "      <td>0.043637</td>\n",
       "      <td>0.031221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035084</td>\n",
       "      <td>0.113329</td>\n",
       "      <td>0.135183</td>\n",
       "      <td>0.102979</td>\n",
       "      <td>0.113108</td>\n",
       "      <td>0.097863</td>\n",
       "      <td>0.128083</td>\n",
       "      <td>0.109425</td>\n",
       "      <td>0.036662</td>\n",
       "      <td>0.036662</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146256003</th>\n",
       "      <td>6845</td>\n",
       "      <td>73443138</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1581799075</td>\n",
       "      <td>6937005</td>\n",
       "      <td>4354</td>\n",
       "      <td>3629</td>\n",
       "      <td>0</td>\n",
       "      <td>1269050894</td>\n",
       "      <td>3141261</td>\n",
       "      <td>4354</td>\n",
       "      <td>3629</td>\n",
       "      <td>0</td>\n",
       "      <td>1501554925</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146256003</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>216</td>\n",
       "      <td>71</td>\n",
       "      <td>50323633</td>\n",
       "      <td>44880037</td>\n",
       "      <td>2674107</td>\n",
       "      <td>2769099</td>\n",
       "      <td>646</td>\n",
       "      <td>2378</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035828</td>\n",
       "      <td>0.075362</td>\n",
       "      <td>0.056931</td>\n",
       "      <td>0.065124</td>\n",
       "      <td>0.068161</td>\n",
       "      <td>0.065125</td>\n",
       "      <td>0.087419</td>\n",
       "      <td>0.031221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070654</td>\n",
       "      <td>0.113329</td>\n",
       "      <td>0.143417</td>\n",
       "      <td>0.122050</td>\n",
       "      <td>0.113108</td>\n",
       "      <td>0.118358</td>\n",
       "      <td>0.140008</td>\n",
       "      <td>0.109425</td>\n",
       "      <td>0.088886</td>\n",
       "      <td>0.088886</td>\n",
       "      <td>0.030826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "146255999         0  73443134      0      0        0           1        59   \n",
       "146256000         0  73443135      0      0        0           1        54   \n",
       "146256001         0  73443136      9      0        0           1         4   \n",
       "146256002    845560  73443137      0      0        0           2        11   \n",
       "146256003      6845  73443138      7      0        0           2        11   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "146255999  1582081341    1362744              5390                833   \n",
       "146256000  1582091134     179475             17747               1467   \n",
       "146256001  1582086464     366281              4386                 80   \n",
       "146256002  1581665518    1030299              4236               4119   \n",
       "146256003  1581799075    6937005              4354               3629   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "146255999              0          1546658592   30848700              5390   \n",
       "146256000              0          1172558525    4950585             17747   \n",
       "146256001              0          1457176980   30312854              4386   \n",
       "146256002              0          1524226898    3141261              4354   \n",
       "146256003              0          1269050894    3141261              4354   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "146255999                833              0          1428007228            0   \n",
       "146256000               1467              0          1272381192            0   \n",
       "146256001                 80              0          1235182992            0   \n",
       "146256002               3629              0          1501554925            1   \n",
       "146256003               3629              0          1501554925            1   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "146255999      0        0                0     0  146255999   2      19   \n",
       "146256000      0        0                0     0  146256000   2      19   \n",
       "146256001      0        0                0     0  146256001   2      19   \n",
       "146256002      0        0                0     0  146256002   2      14   \n",
       "146256003      0        0                0     0  146256003   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "146255999       2        3                 2                            1   \n",
       "146256000       2        5                 0                            0   \n",
       "146256001       2        4                 2                            1   \n",
       "146256002       4        7                 2                            1   \n",
       "146256003       5       20                 2                            1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "146255999                             1                               1   \n",
       "146256000                             1                               0   \n",
       "146256001                             1                               1   \n",
       "146256002                             1                               1   \n",
       "146256003                             1                               1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "146255999                       1                      1   \n",
       "146256000                       1                      0   \n",
       "146256001                       1                      1   \n",
       "146256002                       1                      1   \n",
       "146256003                       1                      1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "146255999                         1          0          76           15   \n",
       "146256000                         0          0         159           29   \n",
       "146256001                         1          0          72           10   \n",
       "146256002                         1          0         161           42   \n",
       "146256003                         1          0         216           71   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "146255999  39170347      34858164          28651          210319   \n",
       "146256000  38847520       1535005         300662          267542   \n",
       "146256001  49748666      44369960        7361311          466019   \n",
       "146256002  50323632      44880036        7551233          985413   \n",
       "146256003  50323633      44880037        2674107         2769099   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "146255999          2793          27536       6         0         0   \n",
       "146256000            11            867      13         0         0   \n",
       "146256001         19261         202536       5         0         0   \n",
       "146256002           112           2712      14         0         0   \n",
       "146256003           646           2378      26         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet  \\\n",
       "146255999         7739                                       NaN   \n",
       "146256000        10748                                  0.144795   \n",
       "146256001        58462                                       NaN   \n",
       "146256002            0                                  0.035828   \n",
       "146256003            0                                  0.035828   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet  \\\n",
       "146255999                                      0.242062   \n",
       "146256000                                      0.111882   \n",
       "146256001                                      0.104071   \n",
       "146256002                                           NaN   \n",
       "146256003                                      0.075362   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet  \\\n",
       "146255999                                     0.196108   \n",
       "146256000                                     0.095624   \n",
       "146256001                                     0.268089   \n",
       "146256002                                     0.087427   \n",
       "146256003                                     0.056931   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet  \\\n",
       "146255999                                 0.157438   \n",
       "146256000                                 0.119549   \n",
       "146256001                                 0.268123   \n",
       "146256002                                 0.065124   \n",
       "146256003                                 0.065124   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet  \\\n",
       "146255999                                 0.149134   \n",
       "146256000                                 0.112607   \n",
       "146256001                                 0.259497   \n",
       "146256002                                 0.068161   \n",
       "146256003                                 0.068161   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet  TE_a_user_id_retweet  \\\n",
       "146255999                                    0.234928              0.207139   \n",
       "146256000                                    0.102531              0.132048   \n",
       "146256001                                    0.376008              0.539948   \n",
       "146256002                                    0.065125              0.043637   \n",
       "146256003                                    0.065125              0.087419   \n",
       "\n",
       "           TE_b_user_id_retweet  TE_tw_hash_retweet  TE_tw_freq_hash_retweet  \\\n",
       "146255999                   NaN                 NaN                      NaN   \n",
       "146256000              0.239516                 NaN                 0.104071   \n",
       "146256001                   NaN                 NaN                      NaN   \n",
       "146256002              0.031221                 NaN                      NaN   \n",
       "146256003              0.031221                 NaN                      NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet  \\\n",
       "146255999                                           0.106849                              \n",
       "146256000                                           0.105555                              \n",
       "146256001                                           0.240498                              \n",
       "146256002                                           0.035084                              \n",
       "146256003                                           0.070654                              \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet  \\\n",
       "146255999                                         0.347383   \n",
       "146256000                                         0.000784   \n",
       "146256001                                         0.504703   \n",
       "146256002                                         0.113329   \n",
       "146256003                                         0.113329   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet  \\\n",
       "146255999                                           0.189820       \n",
       "146256000                                           0.002005       \n",
       "146256001                                           0.386489       \n",
       "146256002                                           0.135183       \n",
       "146256003                                           0.143417       \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet  \\\n",
       "146255999                                           0.162446        \n",
       "146256000                                           0.151419        \n",
       "146256001                                           0.323310        \n",
       "146256002                                           0.102979        \n",
       "146256003                                           0.122050        \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet  \\\n",
       "146255999                                           0.343604               \n",
       "146256000                                           0.002087               \n",
       "146256001                                           0.500458               \n",
       "146256002                                           0.113108               \n",
       "146256003                                           0.113108               \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet  \\\n",
       "146255999                                          0.155693   \n",
       "146256000                                          0.143231   \n",
       "146256001                                          0.319225   \n",
       "146256002                                          0.097863   \n",
       "146256003                                          0.118358   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet  \\\n",
       "146255999                                         0.181927   \n",
       "146256000                                         0.003373   \n",
       "146256001                                         0.380664   \n",
       "146256002                                         0.128083   \n",
       "146256003                                         0.140008   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet  \\\n",
       "146255999                                           0.317300         \n",
       "146256000                                           0.003542         \n",
       "146256001                                           0.478840         \n",
       "146256002                                           0.109425         \n",
       "146256003                                           0.109425         \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet  \\\n",
       "146255999                                      0.154105   \n",
       "146256000                                      0.111300   \n",
       "146256001                                      0.255488   \n",
       "146256002                                      0.036662   \n",
       "146256003                                      0.088886   \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet  \\\n",
       "146255999                                    0.154105   \n",
       "146256000                                    0.111300   \n",
       "146256001                                    0.255488   \n",
       "146256002                                    0.036662   \n",
       "146256003                                    0.088886   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet  \n",
       "146255999                                       0.156461  \n",
       "146256000                                       0.109566  \n",
       "146256001                                       0.262483  \n",
       "146256002                                            NaN  \n",
       "146256003                                       0.030826  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hashtags                                                                          int32\n",
       "tweet_id                                                                          int32\n",
       "media                                                                              int8\n",
       "links                                                                             int32\n",
       "domains                                                                           int32\n",
       "tweet_type                                                                         int8\n",
       "language                                                                           int8\n",
       "timestamp                                                                         int32\n",
       "a_user_id                                                                         int32\n",
       "a_follower_count                                                                  int32\n",
       "a_following_count                                                                 int32\n",
       "a_is_verified                                                                      int8\n",
       "a_account_creation                                                                int32\n",
       "b_user_id                                                                         int32\n",
       "b_follower_count                                                                  int32\n",
       "b_following_count                                                                 int32\n",
       "b_is_verified                                                                      int8\n",
       "b_account_creation                                                                int32\n",
       "b_follows_a                                                                        int8\n",
       "reply                                                                             int32\n",
       "retweet                                                                           int32\n",
       "retweet_comment                                                                   int32\n",
       "like                                                                              int32\n",
       "id                                                                                int32\n",
       "tr                                                                                int32\n",
       "dt_day                                                                             int8\n",
       "dt_dow                                                                             int8\n",
       "dt_hour                                                                            int8\n",
       "a_count_combined                                                                  int32\n",
       "a_user_fer_count_delta_time                                                       int32\n",
       "a_user_fing_count_delta_time                                                      int32\n",
       "a_user_fering_count_delta_time                                                    int32\n",
       "a_user_fing_count_mode                                                            int32\n",
       "a_user_fer_count_mode                                                             int32\n",
       "a_user_fering_count_mode                                                          int32\n",
       "count_ats                                                                         int32\n",
       "count_char                                                                        int32\n",
       "count_words                                                                       int32\n",
       "tw_hash                                                                           int32\n",
       "tw_freq_hash                                                                      int32\n",
       "tw_first_word                                                                     int32\n",
       "tw_second_word                                                                    int32\n",
       "tw_last_word                                                                      int32\n",
       "tw_llast_word                                                                     int32\n",
       "tw_len                                                                            int32\n",
       "tw_hash0                                                                          int32\n",
       "tw_hash1                                                                          int32\n",
       "tw_rt_uhash                                                                       int32\n",
       "TE_b_user_id_tweet_type_language_retweet                                        float32\n",
       "TE_tw_first_word_tweet_type_language_retweet                                    float32\n",
       "TE_tw_last_word_tweet_type_language_retweet                                     float32\n",
       "TE_tw_hash0_tweet_type_language_retweet                                         float32\n",
       "TE_tw_hash1_tweet_type_language_retweet                                         float32\n",
       "TE_tw_rt_uhash_tweet_type_language_retweet                                      float32\n",
       "TE_a_user_id_retweet                                                            float32\n",
       "TE_b_user_id_retweet                                                            float32\n",
       "TE_tw_hash_retweet                                                              float32\n",
       "TE_tw_freq_hash_retweet                                                         float32\n",
       "TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet    float32\n",
       "TE_a_count_combined_tweet_type_language_retweet                                 float32\n",
       "TE_a_user_fer_count_delta_time_media_language_retweet                           float32\n",
       "TE_a_user_fing_count_delta_time_media_language_retweet                          float32\n",
       "TE_a_user_fering_count_delta_time_tweet_type_language_retweet                   float32\n",
       "TE_a_user_fing_count_mode_media_language_retweet                                float32\n",
       "TE_a_user_fer_count_mode_media_language_retweet                                 float32\n",
       "TE_a_user_fering_count_mode_tweet_type_language_retweet                         float32\n",
       "TE_domains_media_tweet_type_language_retweet                                    float32\n",
       "TE_links_media_tweet_type_language_retweet                                      float32\n",
       "TE_hashtags_media_tweet_type_language_retweet                                   float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_memory( train )\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 35 features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['media', 'tweet_type', 'a_follower_count', 'a_following_count',\n",
       "       'a_is_verified', 'b_follower_count', 'b_following_count',\n",
       "       'b_follows_a', 'dt_dow', 'dt_hour', 'count_ats', 'count_char',\n",
       "       'count_words', 'tw_len',\n",
       "       'TE_b_user_id_tweet_type_language_retweet',\n",
       "       'TE_tw_first_word_tweet_type_language_retweet',\n",
       "       'TE_tw_last_word_tweet_type_language_retweet',\n",
       "       'TE_tw_hash0_tweet_type_language_retweet',\n",
       "       'TE_tw_hash1_tweet_type_language_retweet',\n",
       "       'TE_tw_rt_uhash_tweet_type_language_retweet',\n",
       "       'TE_a_user_id_retweet', 'TE_b_user_id_retweet',\n",
       "       'TE_tw_hash_retweet', 'TE_tw_freq_hash_retweet',\n",
       "       'TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet',\n",
       "       'TE_a_count_combined_tweet_type_language_retweet',\n",
       "       'TE_a_user_fer_count_delta_time_media_language_retweet',\n",
       "       'TE_a_user_fing_count_delta_time_media_language_retweet',\n",
       "       'TE_a_user_fering_count_delta_time_tweet_type_language_retweet',\n",
       "       'TE_a_user_fing_count_mode_media_language_retweet',\n",
       "       'TE_a_user_fer_count_mode_media_language_retweet',\n",
       "       'TE_a_user_fering_count_mode_tweet_type_language_retweet',\n",
       "       'TE_domains_media_tweet_type_language_retweet',\n",
       "       'TE_links_media_tweet_type_language_retweet',\n",
       "       'TE_hashtags_media_tweet_type_language_retweet'], dtype='<U76')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = ['reply', 'retweet', 'retweet_comment', 'like']\n",
    "DONT_USE = ['timestamp','a_account_creation','b_account_creation','engage_time',\n",
    "            'a_account_creation', 'b_account_creation',\n",
    "            'fold','tweet_id', \n",
    "            'tr','dt_day','','',\n",
    "            'b_user_id','a_user_id','b_is_verified',\n",
    "            'elapsed_time',\n",
    "            'links','domains','hashtags0','hashtags1',\n",
    "            'hashtags','tweet_hash','dt_second','id',\n",
    "            'tw_hash0',\n",
    "            'tw_hash1',\n",
    "            'tw_rt_uhash',\n",
    "            'same_language', 'nan_language','language',\n",
    "            'tw_hash', 'tw_freq_hash','tw_first_word', 'tw_second_word', 'tw_last_word', 'tw_llast_word',\n",
    "            'ypred','a_count_combined','a_user_fer_count_delta_time','a_user_fing_count_delta_time','a_user_fering_count_delta_time','a_user_fing_count_mode','a_user_fer_count_mode','a_user_fering_count_mode'\n",
    "            \n",
    "           ]\n",
    "DONT_USE += label_names\n",
    "features = [c for c in train.columns if c not in DONT_USE]\n",
    "\n",
    "print('Using %i features:'%(len(features)))\n",
    "np.asarray(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
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       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
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       "      <th>b_is_verified</th>\n",
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       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
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       "      <th>like</th>\n",
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       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
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       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
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       "      <th>TE_a_user_fing_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet</th>\n",
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       "      <th>TE_domains_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "146255999         0  73443134      0      0        0           1        59   \n",
       "146256000         0  73443135      0      0        0           1        54   \n",
       "146256001         0  73443136      9      0        0           1         4   \n",
       "146256002    845560  73443137      0      0        0           2        11   \n",
       "146256003      6845  73443138      7      0        0           2        11   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "146255999  1582081341    1362744              5390                833   \n",
       "146256000  1582091134     179475             17747               1467   \n",
       "146256001  1582086464     366281              4386                 80   \n",
       "146256002  1581665518    1030299              4236               4119   \n",
       "146256003  1581799075    6937005              4354               3629   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "146255999              0          1546658592   30848700              5390   \n",
       "146256000              0          1172558525    4950585             17747   \n",
       "146256001              0          1457176980   30312854              4386   \n",
       "146256002              0          1524226898    3141261              4354   \n",
       "146256003              0          1269050894    3141261              4354   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "146255999                833              0          1428007228            0   \n",
       "146256000               1467              0          1272381192            0   \n",
       "146256001                 80              0          1235182992            0   \n",
       "146256002               3629              0          1501554925            1   \n",
       "146256003               3629              0          1501554925            1   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "146255999      0        0                0     0  146255999   2      19   \n",
       "146256000      0        0                0     0  146256000   2      19   \n",
       "146256001      0        0                0     0  146256001   2      19   \n",
       "146256002      0        0                0     0  146256002   2      14   \n",
       "146256003      0        0                0     0  146256003   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "146255999       2        3                 2                            1   \n",
       "146256000       2        5                 0                            0   \n",
       "146256001       2        4                 2                            1   \n",
       "146256002       4        7                 2                            1   \n",
       "146256003       5       20                 2                            1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "146255999                             1                               1   \n",
       "146256000                             1                               0   \n",
       "146256001                             1                               1   \n",
       "146256002                             1                               1   \n",
       "146256003                             1                               1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "146255999                       1                      1   \n",
       "146256000                       1                      0   \n",
       "146256001                       1                      1   \n",
       "146256002                       1                      1   \n",
       "146256003                       1                      1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "146255999                         1          0          76           15   \n",
       "146256000                         0          0         159           29   \n",
       "146256001                         1          0          72           10   \n",
       "146256002                         1          0         161           42   \n",
       "146256003                         1          0         216           71   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "146255999  39170347      34858164          28651          210319   \n",
       "146256000  38847520       1535005         300662          267542   \n",
       "146256001  49748666      44369960        7361311          466019   \n",
       "146256002  50323632      44880036        7551233          985413   \n",
       "146256003  50323633      44880037        2674107         2769099   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "146255999          2793          27536       6         0         0   \n",
       "146256000            11            867      13         0         0   \n",
       "146256001         19261         202536       5         0         0   \n",
       "146256002           112           2712      14         0         0   \n",
       "146256003           646           2378      26         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet  \\\n",
       "146255999         7739                                       NaN   \n",
       "146256000        10748                                  0.144795   \n",
       "146256001        58462                                       NaN   \n",
       "146256002            0                                  0.035828   \n",
       "146256003            0                                  0.035828   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet  \\\n",
       "146255999                                      0.242062   \n",
       "146256000                                      0.111882   \n",
       "146256001                                      0.104071   \n",
       "146256002                                           NaN   \n",
       "146256003                                      0.075362   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet  \\\n",
       "146255999                                     0.196108   \n",
       "146256000                                     0.095624   \n",
       "146256001                                     0.268089   \n",
       "146256002                                     0.087427   \n",
       "146256003                                     0.056931   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet  \\\n",
       "146255999                                 0.157438   \n",
       "146256000                                 0.119549   \n",
       "146256001                                 0.268123   \n",
       "146256002                                 0.065124   \n",
       "146256003                                 0.065124   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet  \\\n",
       "146255999                                 0.149134   \n",
       "146256000                                 0.112607   \n",
       "146256001                                 0.259497   \n",
       "146256002                                 0.068161   \n",
       "146256003                                 0.068161   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet  TE_a_user_id_retweet  \\\n",
       "146255999                                    0.234928              0.207139   \n",
       "146256000                                    0.102531              0.132048   \n",
       "146256001                                    0.376008              0.539948   \n",
       "146256002                                    0.065125              0.043637   \n",
       "146256003                                    0.065125              0.087419   \n",
       "\n",
       "           TE_b_user_id_retweet  TE_tw_hash_retweet  TE_tw_freq_hash_retweet  \\\n",
       "146255999                   NaN                 NaN                      NaN   \n",
       "146256000              0.239516                 NaN                 0.104071   \n",
       "146256001                   NaN                 NaN                      NaN   \n",
       "146256002              0.031221                 NaN                      NaN   \n",
       "146256003              0.031221                 NaN                      NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet  \\\n",
       "146255999                                           0.106849                              \n",
       "146256000                                           0.105555                              \n",
       "146256001                                           0.240498                              \n",
       "146256002                                           0.035084                              \n",
       "146256003                                           0.070654                              \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet  \\\n",
       "146255999                                         0.347383   \n",
       "146256000                                         0.000784   \n",
       "146256001                                         0.504703   \n",
       "146256002                                         0.113329   \n",
       "146256003                                         0.113329   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet  \\\n",
       "146255999                                           0.189820       \n",
       "146256000                                           0.002005       \n",
       "146256001                                           0.386489       \n",
       "146256002                                           0.135183       \n",
       "146256003                                           0.143417       \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet  \\\n",
       "146255999                                           0.162446        \n",
       "146256000                                           0.151419        \n",
       "146256001                                           0.323310        \n",
       "146256002                                           0.102979        \n",
       "146256003                                           0.122050        \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet  \\\n",
       "146255999                                           0.343604               \n",
       "146256000                                           0.002087               \n",
       "146256001                                           0.500458               \n",
       "146256002                                           0.113108               \n",
       "146256003                                           0.113108               \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet  \\\n",
       "146255999                                          0.155693   \n",
       "146256000                                          0.143231   \n",
       "146256001                                          0.319225   \n",
       "146256002                                          0.097863   \n",
       "146256003                                          0.118358   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet  \\\n",
       "146255999                                         0.181927   \n",
       "146256000                                         0.003373   \n",
       "146256001                                         0.380664   \n",
       "146256002                                         0.128083   \n",
       "146256003                                         0.140008   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet  \\\n",
       "146255999                                           0.317300         \n",
       "146256000                                           0.003542         \n",
       "146256001                                           0.478840         \n",
       "146256002                                           0.109425         \n",
       "146256003                                           0.109425         \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet  \\\n",
       "146255999                                      0.154105   \n",
       "146256000                                      0.111300   \n",
       "146256001                                      0.255488   \n",
       "146256002                                      0.036662   \n",
       "146256003                                      0.088886   \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet  \\\n",
       "146255999                                    0.154105   \n",
       "146256000                                    0.111300   \n",
       "146256001                                    0.255488   \n",
       "146256002                                    0.036662   \n",
       "146256003                                    0.088886   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet  \n",
       "146255999                                       0.156461  \n",
       "146256000                                       0.109566  \n",
       "146256001                                       0.262483  \n",
       "146256002                                            NaN  \n",
       "146256003                                       0.030826  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60714530, 69), (60671901, 69), (12434735, 69), (12434838, 69))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2020)\n",
    "SEED = 1\n",
    "\n",
    "valid = train.loc[ ((train.tweet_id % 2)==1)&(train.tr==0) ]\n",
    "gc.collect()\n",
    "\n",
    "test0 = train.loc[ (train.tr==1) ]\n",
    "gc.collect()\n",
    "\n",
    "test1 = train.loc[ (train.tr==2) ]\n",
    "gc.collect()\n",
    "\n",
    "train = train.loc[ ((train.tweet_id % 2)==0)&(train.tr==0) ]\n",
    "gc.collect()\n",
    "\n",
    "train.shape, valid.shape, test0.shape, test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Version 1.1.0\n"
     ]
    }
   ],
   "source": [
    "xgb_parms = { \n",
    "    'max_depth':8, \n",
    "    'learning_rate':0.025, \n",
    "    'subsample':0.85,\n",
    "    'colsample_bytree':0.35, \n",
    "    'eval_metric':'logloss',\n",
    "    'objective':'binary:logistic',\n",
    "    'tree_method':'gpu_hist',\n",
    "    #'predictor': 'gpu_predictor',\n",
    "    'seed': 1,\n",
    "}\n",
    "\n",
    "import xgboost as xgb\n",
    "print('XGB Version',xgb.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE TRAIN AND VALIDATION SETS\n",
    "RMV = [c for c in DONT_USE if c in train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########################\n",
      "### retweet\n",
      "#########################\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LEarning rates for 'reply', 'retweet', 'retweet_comment', 'like'\n",
    "LR = [0.05,0.03,0.07,0.01]\n",
    "\n",
    "#Like\n",
    "xgb_parms['learning_rate'] = LR[TARGET_id]\n",
    "\n",
    "print('#'*25);print('###',TARGET);print('#'*25)\n",
    "\n",
    "dtrain = xgb.DMatrix(data=train.drop(RMV, axis=1) ,label=train[TARGET].values)\n",
    "gc.collect()\n",
    "\n",
    "model = xgb.train(xgb_parms, \n",
    "                  dtrain=dtrain,\n",
    "                  num_boost_round=500,\n",
    "                 ) \n",
    "\n",
    "del dtrain\n",
    "gc.collect()  \n",
    "\n",
    "#save model\n",
    "joblib.dump(model, 'model-'+TARGET+'-1.xgb' ) \n",
    "del model\n",
    "gc.collect()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain = xgb.DMatrix(data=valid.drop(RMV, axis=1) ,label=valid[TARGET].values)\n",
    "gc.collect()\n",
    "\n",
    "model = xgb.train(xgb_parms, \n",
    "                  dtrain=dtrain,\n",
    "                  num_boost_round=500,\n",
    "                 ) \n",
    "\n",
    "del dtrain\n",
    "gc.collect()  \n",
    "\n",
    "#save model\n",
    "joblib.dump(model, 'model-'+TARGET+'-2.xgb' ) \n",
    "\n",
    "del model\n",
    "gc.collect()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load xgb1\n"
     ]
    }
   ],
   "source": [
    "print('load xgb1')\n",
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "dvalid = xgb.DMatrix(data=valid.drop(RMV, axis=1))\n",
    "valid['ypred'] = model.predict(dvalid)\n",
    "del dvalid, model; _=gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load xgb2\n"
     ]
    }
   ],
   "source": [
    "print('load xgb2')\n",
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "dtrain = xgb.DMatrix(data=train.drop(RMV, axis=1))\n",
    "train['ypred'] = model.predict(dtrain)\n",
    "del dtrain, model; _=gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOF RCE:\n",
      "39.4946679821099\n",
      "39.45238129892129\n"
     ]
    }
   ],
   "source": [
    "print(\"OOF RCE:\")\n",
    "print( compute_rce( valid[TARGET].values, valid['ypred'].values ) )\n",
    "print( compute_rce( train[TARGET].values, train['ypred'].values ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4302e54550>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt0 = train.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt1 = valid.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt0.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "dt1.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "\n",
    "dt0['ypred2'] = dt1['ypred'].values\n",
    "\n",
    "dt0[['ypred','ypred2']].plot(  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4302eb99e8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12,12)\n",
    "\n",
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "plot_importance(model, height=1.0, show_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Public val.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "test0.sort_values( 'id', inplace=True )\n",
    "sub = pd.read_csv('../preprocessings/sample_submission_public.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
       "      <th>tw_hash0</th>\n",
       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_id_retweet</th>\n",
       "      <th>TE_b_user_id_retweet</th>\n",
       "      <th>TE_tw_hash_retweet</th>\n",
       "      <th>TE_tw_freq_hash_retweet</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121386431</th>\n",
       "      <td>0</td>\n",
       "      <td>57733249</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>1581703126</td>\n",
       "      <td>534117</td>\n",
       "      <td>13919</td>\n",
       "      <td>1214</td>\n",
       "      <td>0</td>\n",
       "      <td>1448292186</td>\n",
       "      <td>3617447</td>\n",
       "      <td>8794</td>\n",
       "      <td>2134</td>\n",
       "      <td>0</td>\n",
       "      <td>1520948869</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386431</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>38691181</td>\n",
       "      <td>34413698</td>\n",
       "      <td>562265</td>\n",
       "      <td>296463</td>\n",
       "      <td>83986</td>\n",
       "      <td>110811</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.122519</td>\n",
       "      <td>0.127419</td>\n",
       "      <td>0.096040</td>\n",
       "      <td>0.095944</td>\n",
       "      <td>0.096044</td>\n",
       "      <td>0.064392</td>\n",
       "      <td>0.091062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.174686</td>\n",
       "      <td>0.155413</td>\n",
       "      <td>0.110555</td>\n",
       "      <td>0.171882</td>\n",
       "      <td>0.009589</td>\n",
       "      <td>0.147760</td>\n",
       "      <td>0.165577</td>\n",
       "      <td>0.088073</td>\n",
       "      <td>0.088073</td>\n",
       "      <td>0.070502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386432</th>\n",
       "      <td>0</td>\n",
       "      <td>57733250</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1582021842</td>\n",
       "      <td>2721240</td>\n",
       "      <td>186</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1263078566</td>\n",
       "      <td>12365145</td>\n",
       "      <td>111835</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1335110299</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386432</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>38691182</td>\n",
       "      <td>34413699</td>\n",
       "      <td>7376000</td>\n",
       "      <td>4871859</td>\n",
       "      <td>348771</td>\n",
       "      <td>1398132</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102048</td>\n",
       "      <td>0.104071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070500</td>\n",
       "      <td>0.108894</td>\n",
       "      <td>0.104526</td>\n",
       "      <td>0.070500</td>\n",
       "      <td>0.099340</td>\n",
       "      <td>0.099546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.134671</td>\n",
       "      <td>0.092170</td>\n",
       "      <td>0.131169</td>\n",
       "      <td>0.131169</td>\n",
       "      <td>0.092170</td>\n",
       "      <td>0.131169</td>\n",
       "      <td>0.131169</td>\n",
       "      <td>0.092170</td>\n",
       "      <td>0.127259</td>\n",
       "      <td>0.127259</td>\n",
       "      <td>0.122412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386433</th>\n",
       "      <td>0</td>\n",
       "      <td>57733251</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1581734918</td>\n",
       "      <td>2023199</td>\n",
       "      <td>249849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1356488269</td>\n",
       "      <td>28952089</td>\n",
       "      <td>249849</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1503940711</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386433</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>38691183</td>\n",
       "      <td>18372275</td>\n",
       "      <td>16591</td>\n",
       "      <td>29975</td>\n",
       "      <td>1205</td>\n",
       "      <td>23578</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080944</td>\n",
       "      <td>0.036425</td>\n",
       "      <td>0.105246</td>\n",
       "      <td>0.114281</td>\n",
       "      <td>0.105234</td>\n",
       "      <td>0.063710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104071</td>\n",
       "      <td>0.199067</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>0.224013</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.219285</td>\n",
       "      <td>0.006178</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>0.178308</td>\n",
       "      <td>0.178308</td>\n",
       "      <td>0.118749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386434</th>\n",
       "      <td>0</td>\n",
       "      <td>57733252</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1581913613</td>\n",
       "      <td>2816974</td>\n",
       "      <td>517</td>\n",
       "      <td>407</td>\n",
       "      <td>0</td>\n",
       "      <td>1449096567</td>\n",
       "      <td>13774342</td>\n",
       "      <td>574475</td>\n",
       "      <td>2656</td>\n",
       "      <td>0</td>\n",
       "      <td>1396311956</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386434</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>16</td>\n",
       "      <td>38691184</td>\n",
       "      <td>34413700</td>\n",
       "      <td>7376001</td>\n",
       "      <td>165976</td>\n",
       "      <td>4845</td>\n",
       "      <td>16174</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2446</td>\n",
       "      <td>0.113767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.091928</td>\n",
       "      <td>0.119549</td>\n",
       "      <td>0.112607</td>\n",
       "      <td>0.138078</td>\n",
       "      <td>0.068296</td>\n",
       "      <td>0.099546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.143886</td>\n",
       "      <td>0.269482</td>\n",
       "      <td>0.193622</td>\n",
       "      <td>0.164088</td>\n",
       "      <td>0.265449</td>\n",
       "      <td>0.159599</td>\n",
       "      <td>0.188355</td>\n",
       "      <td>0.243212</td>\n",
       "      <td>0.126007</td>\n",
       "      <td>0.126007</td>\n",
       "      <td>0.126332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121386435</th>\n",
       "      <td>0</td>\n",
       "      <td>57733253</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>1581565745</td>\n",
       "      <td>366629</td>\n",
       "      <td>19578</td>\n",
       "      <td>273</td>\n",
       "      <td>1</td>\n",
       "      <td>1236181798</td>\n",
       "      <td>11208153</td>\n",
       "      <td>218811</td>\n",
       "      <td>4980</td>\n",
       "      <td>0</td>\n",
       "      <td>1298646801</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121386435</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>10</td>\n",
       "      <td>38691185</td>\n",
       "      <td>34413701</td>\n",
       "      <td>4769376</td>\n",
       "      <td>56375</td>\n",
       "      <td>852</td>\n",
       "      <td>51742</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080944</td>\n",
       "      <td>0.060050</td>\n",
       "      <td>0.096040</td>\n",
       "      <td>0.095944</td>\n",
       "      <td>0.096044</td>\n",
       "      <td>0.052190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056582</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.001266</td>\n",
       "      <td>0.110555</td>\n",
       "      <td>0.000644</td>\n",
       "      <td>0.104499</td>\n",
       "      <td>0.003411</td>\n",
       "      <td>0.003184</td>\n",
       "      <td>0.088073</td>\n",
       "      <td>0.088073</td>\n",
       "      <td>0.070502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "121386431         0  57733249      5      0        0           2        54   \n",
       "121386432         0  57733250      7      0        0           1        47   \n",
       "121386433         0  57733251      1      0        0           2        13   \n",
       "121386434         0  57733252      7      0        0           1        54   \n",
       "121386435         0  57733253      5      0        0           2        54   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "121386431  1581703126     534117             13919               1214   \n",
       "121386432  1582021842    2721240               186                100   \n",
       "121386433  1581734918    2023199            249849                  1   \n",
       "121386434  1581913613    2816974               517                407   \n",
       "121386435  1581565745     366629             19578                273   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "121386431              0          1448292186    3617447              8794   \n",
       "121386432              0          1263078566   12365145            111835   \n",
       "121386433              0          1356488269   28952089            249849   \n",
       "121386434              0          1449096567   13774342            574475   \n",
       "121386435              1          1236181798   11208153            218811   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "121386431               2134              0          1520948869            1   \n",
       "121386432                  3              0          1335110299            0   \n",
       "121386433                  1              0          1503940711            0   \n",
       "121386434               2656              0          1396311956            1   \n",
       "121386435               4980              0          1298646801            0   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "121386431      0        0                0     0  121386431   1      14   \n",
       "121386432      0        0                0     0  121386432   1      18   \n",
       "121386433      0        0                0     0  121386433   1      15   \n",
       "121386434      0        0                0     0  121386434   1      17   \n",
       "121386435      0        0                0     0  121386435   1      13   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "121386431       4       17                 6                            1   \n",
       "121386432       1       10                -9                           -9   \n",
       "121386433       5        2                 0                            0   \n",
       "121386434       0        4                 2                            1   \n",
       "121386435       3        3                 0                            0   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "121386431                             1                               1   \n",
       "121386432                            -9                              -9   \n",
       "121386433                             1                               0   \n",
       "121386434                             1                               1   \n",
       "121386435                             1                               0   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "121386431                       0                      1   \n",
       "121386432                      -9                     -9   \n",
       "121386433                       1                      0   \n",
       "121386434                       1                      1   \n",
       "121386435                       1                      0   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "121386431                         1          0          55            5   \n",
       "121386432                        -9          0          57            5   \n",
       "121386433                         0          0          49            5   \n",
       "121386434                         1          0         104           16   \n",
       "121386435                         0          0          82           10   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "121386431  38691181      34413698         562265          296463   \n",
       "121386432  38691182      34413699        7376000         4871859   \n",
       "121386433  38691183      18372275          16591           29975   \n",
       "121386434  38691184      34413700        7376001          165976   \n",
       "121386435  38691185      34413701        4769376           56375   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "121386431         83986         110811       3         0         0   \n",
       "121386432        348771        1398132       2         0         0   \n",
       "121386433          1205          23578       2         0         0   \n",
       "121386434          4845          16174       7         0         0   \n",
       "121386435           852          51742       3         0         0   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet  \\\n",
       "121386431            0                                       NaN   \n",
       "121386432       102048                                  0.104071   \n",
       "121386433            0                                       NaN   \n",
       "121386434         2446                                  0.113767   \n",
       "121386435            0                                       NaN   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet  \\\n",
       "121386431                                      0.122519   \n",
       "121386432                                           NaN   \n",
       "121386433                                      0.080944   \n",
       "121386434                                           NaN   \n",
       "121386435                                      0.080944   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet  \\\n",
       "121386431                                     0.127419   \n",
       "121386432                                     0.070500   \n",
       "121386433                                     0.036425   \n",
       "121386434                                     0.091928   \n",
       "121386435                                     0.060050   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet  \\\n",
       "121386431                                 0.096040   \n",
       "121386432                                 0.108894   \n",
       "121386433                                 0.105246   \n",
       "121386434                                 0.119549   \n",
       "121386435                                 0.096040   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet  \\\n",
       "121386431                                 0.095944   \n",
       "121386432                                 0.104526   \n",
       "121386433                                 0.114281   \n",
       "121386434                                 0.112607   \n",
       "121386435                                 0.095944   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet  TE_a_user_id_retweet  \\\n",
       "121386431                                    0.096044              0.064392   \n",
       "121386432                                    0.070500              0.099340   \n",
       "121386433                                    0.105234              0.063710   \n",
       "121386434                                    0.138078              0.068296   \n",
       "121386435                                    0.096044              0.052190   \n",
       "\n",
       "           TE_b_user_id_retweet  TE_tw_hash_retweet  TE_tw_freq_hash_retweet  \\\n",
       "121386431              0.091062                 NaN                      NaN   \n",
       "121386432              0.099546                 NaN                      NaN   \n",
       "121386433                   NaN                 NaN                 0.104071   \n",
       "121386434              0.099546                 NaN                      NaN   \n",
       "121386435                   NaN                 NaN                      NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet  \\\n",
       "121386431                                           0.122908                              \n",
       "121386432                                           0.134671                              \n",
       "121386433                                           0.199067                              \n",
       "121386434                                           0.143886                              \n",
       "121386435                                           0.056582                              \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet  \\\n",
       "121386431                                         0.174686   \n",
       "121386432                                         0.092170   \n",
       "121386433                                         0.000434   \n",
       "121386434                                         0.269482   \n",
       "121386435                                         0.000171   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet  \\\n",
       "121386431                                           0.155413       \n",
       "121386432                                           0.131169       \n",
       "121386433                                           0.005346       \n",
       "121386434                                           0.193622       \n",
       "121386435                                           0.001266       \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet  \\\n",
       "121386431                                           0.110555        \n",
       "121386432                                           0.131169        \n",
       "121386433                                           0.224013        \n",
       "121386434                                           0.164088        \n",
       "121386435                                           0.110555        \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet  \\\n",
       "121386431                                           0.171882               \n",
       "121386432                                           0.092170               \n",
       "121386433                                           0.002643               \n",
       "121386434                                           0.265449               \n",
       "121386435                                           0.000644               \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet  \\\n",
       "121386431                                          0.009589   \n",
       "121386432                                          0.131169   \n",
       "121386433                                          0.219285   \n",
       "121386434                                          0.159599   \n",
       "121386435                                          0.104499   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet  \\\n",
       "121386431                                         0.147760   \n",
       "121386432                                         0.131169   \n",
       "121386433                                         0.006178   \n",
       "121386434                                         0.188355   \n",
       "121386435                                         0.003411   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet  \\\n",
       "121386431                                           0.165577         \n",
       "121386432                                           0.092170         \n",
       "121386433                                           0.003689         \n",
       "121386434                                           0.243212         \n",
       "121386435                                           0.003184         \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet  \\\n",
       "121386431                                      0.088073   \n",
       "121386432                                      0.127259   \n",
       "121386433                                      0.178308   \n",
       "121386434                                      0.126007   \n",
       "121386435                                      0.088073   \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet  \\\n",
       "121386431                                    0.088073   \n",
       "121386432                                    0.127259   \n",
       "121386433                                    0.178308   \n",
       "121386434                                    0.126007   \n",
       "121386435                                    0.088073   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet  \n",
       "121386431                                       0.070502  \n",
       "121386432                                       0.122412  \n",
       "121386433                                       0.118749  \n",
       "121386434                                       0.126332  \n",
       "121386435                                       0.070502  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(data=test0.drop(RMV, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434735"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-1.xgb')\n",
    "ytest = model.predict(dtest,ntree_limit=500)\n",
    "del model; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434735"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "ytest += model.predict(dtest)\n",
    "ytest /= 2.\n",
    "del model, dtest; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_day\n",
      "13    0.101950\n",
      "14    0.102981\n",
      "15    0.100704\n",
      "16    0.100838\n",
      "17    0.107496\n",
      "18    0.140063\n",
      "19    0.093540\n",
      "Name: ypred, dtype: float32\n",
      "0.097273864 0.13671198 0.12802711 0.097728975 0.71152407 0.76334596 0.73743498827292\n"
     ]
    }
   ],
   "source": [
    "test0['ypred'] = ytest\n",
    "\n",
    "print( test0.groupby('dt_day')['ypred'].mean() )\n",
    "m17 = test0.loc[ (test0.dt_day==17)&(test0.dt_hour==23), 'ypred' ].mean()\n",
    "m18a = test0.loc[ (test0.dt_day==18)&(test0.dt_hour==0), 'ypred' ].mean()\n",
    "m18b = test0.loc[ (test0.dt_day==18)&(test0.dt_hour==23), 'ypred' ].mean()\n",
    "m19 = test0.loc[ (test0.dt_day==19)&(test0.dt_hour==0), 'ypred' ].mean()\n",
    "\n",
    "print( m17, m18a, m18b, m19, m17/m18a, m19/m18b,0.5*m17/m18a+ 0.5*m19/m18b )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10170355"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test0.loc[ test0.dt_day==18 ,'ypred'] *=  ( 0.5*m17/m18a+ 0.5*m19/m18b )\n",
    "\n",
    "sub['prediction'] = test0['ypred'].values\n",
    "sub.to_csv('submissions/xgb-final-'+TARGET+'-public-1.csv',header=None, index=False)\n",
    "sub['prediction'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Private competition_test.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1.sort_values( 'id', inplace=True )\n",
    "sub = pd.read_csv('../preprocessings/sample_submission_private.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>media</th>\n",
       "      <th>links</th>\n",
       "      <th>domains</th>\n",
       "      <th>tweet_type</th>\n",
       "      <th>language</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>a_user_id</th>\n",
       "      <th>a_follower_count</th>\n",
       "      <th>a_following_count</th>\n",
       "      <th>a_is_verified</th>\n",
       "      <th>a_account_creation</th>\n",
       "      <th>b_user_id</th>\n",
       "      <th>b_follower_count</th>\n",
       "      <th>b_following_count</th>\n",
       "      <th>b_is_verified</th>\n",
       "      <th>b_account_creation</th>\n",
       "      <th>b_follows_a</th>\n",
       "      <th>reply</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_comment</th>\n",
       "      <th>like</th>\n",
       "      <th>id</th>\n",
       "      <th>tr</th>\n",
       "      <th>dt_day</th>\n",
       "      <th>dt_dow</th>\n",
       "      <th>dt_hour</th>\n",
       "      <th>a_count_combined</th>\n",
       "      <th>a_user_fer_count_delta_time</th>\n",
       "      <th>a_user_fing_count_delta_time</th>\n",
       "      <th>a_user_fering_count_delta_time</th>\n",
       "      <th>a_user_fing_count_mode</th>\n",
       "      <th>a_user_fer_count_mode</th>\n",
       "      <th>a_user_fering_count_mode</th>\n",
       "      <th>count_ats</th>\n",
       "      <th>count_char</th>\n",
       "      <th>count_words</th>\n",
       "      <th>tw_hash</th>\n",
       "      <th>tw_freq_hash</th>\n",
       "      <th>tw_first_word</th>\n",
       "      <th>tw_second_word</th>\n",
       "      <th>tw_last_word</th>\n",
       "      <th>tw_llast_word</th>\n",
       "      <th>tw_len</th>\n",
       "      <th>tw_hash0</th>\n",
       "      <th>tw_hash1</th>\n",
       "      <th>tw_rt_uhash</th>\n",
       "      <th>TE_b_user_id_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_first_word_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_last_word_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_hash0_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_hash1_tweet_type_language_retweet</th>\n",
       "      <th>TE_tw_rt_uhash_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_id_retweet</th>\n",
       "      <th>TE_b_user_id_retweet</th>\n",
       "      <th>TE_tw_hash_retweet</th>\n",
       "      <th>TE_tw_freq_hash_retweet</th>\n",
       "      <th>TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet</th>\n",
       "      <th>TE_a_count_combined_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_delta_time_media_language_retweet</th>\n",
       "      <th>TE_a_user_fing_count_delta_time_media_language_retweet</th>\n",
       "      <th>TE_a_user_fering_count_delta_time_tweet_type_language_retweet</th>\n",
       "      <th>TE_a_user_fing_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fer_count_mode_media_language_retweet</th>\n",
       "      <th>TE_a_user_fering_count_mode_tweet_type_language_retweet</th>\n",
       "      <th>TE_domains_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_links_media_tweet_type_language_retweet</th>\n",
       "      <th>TE_hashtags_media_tweet_type_language_retweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.135183</td>\n",
       "      <td>0.102979</td>\n",
       "      <td>0.113108</td>\n",
       "      <td>0.097863</td>\n",
       "      <td>0.128083</td>\n",
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       "      <td>0.036662</td>\n",
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       "      <td>0.249861</td>\n",
       "      <td>0.119008</td>\n",
       "      <td>0.119008</td>\n",
       "      <td>0.050825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133821168</th>\n",
       "      <td>0</td>\n",
       "      <td>66410931</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1582046459</td>\n",
       "      <td>199258</td>\n",
       "      <td>4312</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>1494251627</td>\n",
       "      <td>879586</td>\n",
       "      <td>4312</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>1540395738</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821168</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>18</td>\n",
       "      <td>45410186</td>\n",
       "      <td>40509200</td>\n",
       "      <td>7627046</td>\n",
       "      <td>1151943</td>\n",
       "      <td>2638</td>\n",
       "      <td>18369</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139857</td>\n",
       "      <td>0.144795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066836</td>\n",
       "      <td>0.119549</td>\n",
       "      <td>0.112607</td>\n",
       "      <td>0.181900</td>\n",
       "      <td>0.085385</td>\n",
       "      <td>0.306183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.131317</td>\n",
       "      <td>0.097242</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.097242</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.097242</td>\n",
       "      <td>0.116451</td>\n",
       "      <td>0.116451</td>\n",
       "      <td>0.115911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133821169</th>\n",
       "      <td>26906</td>\n",
       "      <td>66410932</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1582083666</td>\n",
       "      <td>924614</td>\n",
       "      <td>272</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1559086871</td>\n",
       "      <td>647103</td>\n",
       "      <td>272</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1432084055</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821169</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>9</td>\n",
       "      <td>39837068</td>\n",
       "      <td>35471058</td>\n",
       "      <td>6610718</td>\n",
       "      <td>1008990</td>\n",
       "      <td>69708</td>\n",
       "      <td>35969</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.123103</td>\n",
       "      <td>0.268123</td>\n",
       "      <td>0.259497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.147141</td>\n",
       "      <td>0.152514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.305488</td>\n",
       "      <td>0.504703</td>\n",
       "      <td>0.319379</td>\n",
       "      <td>0.253471</td>\n",
       "      <td>0.500458</td>\n",
       "      <td>0.249619</td>\n",
       "      <td>0.311038</td>\n",
       "      <td>0.478840</td>\n",
       "      <td>0.243152</td>\n",
       "      <td>0.243152</td>\n",
       "      <td>0.084057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133821170</th>\n",
       "      <td>518727</td>\n",
       "      <td>66410933</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1581779241</td>\n",
       "      <td>4280276</td>\n",
       "      <td>1020</td>\n",
       "      <td>2097</td>\n",
       "      <td>0</td>\n",
       "      <td>1468438879</td>\n",
       "      <td>29849501</td>\n",
       "      <td>405774</td>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "      <td>1385502405</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133821170</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>167</td>\n",
       "      <td>24</td>\n",
       "      <td>45410187</td>\n",
       "      <td>40509201</td>\n",
       "      <td>7773604</td>\n",
       "      <td>5355936</td>\n",
       "      <td>3030</td>\n",
       "      <td>2103</td>\n",
       "      <td>9</td>\n",
       "      <td>6521258</td>\n",
       "      <td>5953099</td>\n",
       "      <td>325751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.097428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095021</td>\n",
       "      <td>0.091062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088363</td>\n",
       "      <td>0.050882</td>\n",
       "      <td>0.048713</td>\n",
       "      <td>0.068570</td>\n",
       "      <td>0.032003</td>\n",
       "      <td>0.070561</td>\n",
       "      <td>0.062146</td>\n",
       "      <td>0.034965</td>\n",
       "      <td>0.095762</td>\n",
       "      <td>0.095762</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hashtags  tweet_id  media  links  domains  tweet_type  language  \\\n",
       "133821166         0  66410929      0      0        0           2        11   \n",
       "133821167     25339  66410930      0      0        0           1        11   \n",
       "133821168         0  66410931      9      0        0           1        54   \n",
       "133821169     26906  66410932      5      0        0           1         4   \n",
       "133821170    518727  66410933      5      0        0           1        54   \n",
       "\n",
       "            timestamp  a_user_id  a_follower_count  a_following_count  \\\n",
       "133821166  1581759640   12167358               119                125   \n",
       "133821167  1581668217    9371104               189                264   \n",
       "133821168  1582046459     199258              4312                660   \n",
       "133821169  1582083666     924614               272                185   \n",
       "133821170  1581779241    4280276              1020               2097   \n",
       "\n",
       "           a_is_verified  a_account_creation  b_user_id  b_follower_count  \\\n",
       "133821166              0          1571666822    6237935               189   \n",
       "133821167              0          1575966890    6237935               189   \n",
       "133821168              0          1494251627     879586              4312   \n",
       "133821169              0          1559086871     647103               272   \n",
       "133821170              0          1468438879   29849501            405774   \n",
       "\n",
       "           b_following_count  b_is_verified  b_account_creation  b_follows_a  \\\n",
       "133821166                264              0          1478011810            1   \n",
       "133821167                264              0          1478011810            1   \n",
       "133821168                660              0          1540395738            1   \n",
       "133821169                185              0          1432084055            1   \n",
       "133821170                974              0          1385502405            0   \n",
       "\n",
       "           reply  retweet  retweet_comment  like         id  tr  dt_day  \\\n",
       "133821166      0        0                0     0  133821166   2      15   \n",
       "133821167      0        0                0     0  133821167   2      14   \n",
       "133821168      0        0                0     0  133821168   2      18   \n",
       "133821169      0        0                0     0  133821169   2      19   \n",
       "133821170      0        0                0     0  133821170   2      15   \n",
       "\n",
       "           dt_dow  dt_hour  a_count_combined  a_user_fer_count_delta_time  \\\n",
       "133821166       5        9                 2                            1   \n",
       "133821167       4        8                 2                            1   \n",
       "133821168       1       17                -9                           -9   \n",
       "133821169       2        3                 2                            1   \n",
       "133821170       5       15                10                           -1   \n",
       "\n",
       "           a_user_fing_count_delta_time  a_user_fering_count_delta_time  \\\n",
       "133821166                             1                               1   \n",
       "133821167                             1                               1   \n",
       "133821168                            -9                              -9   \n",
       "133821169                             1                               1   \n",
       "133821170                            -1                              -1   \n",
       "\n",
       "           a_user_fing_count_mode  a_user_fer_count_mode  \\\n",
       "133821166                       1                      1   \n",
       "133821167                       1                      1   \n",
       "133821168                      -9                     -9   \n",
       "133821169                       1                      1   \n",
       "133821170                      -1                     -1   \n",
       "\n",
       "           a_user_fering_count_mode  count_ats  count_char  count_words  \\\n",
       "133821166                         1          0          59           15   \n",
       "133821167                         1          0         189           36   \n",
       "133821168                        -9          0         129           18   \n",
       "133821169                         1          0          73            9   \n",
       "133821170                        -1          5         167           24   \n",
       "\n",
       "            tw_hash  tw_freq_hash  tw_first_word  tw_second_word  \\\n",
       "133821166  45410185      40509199         163328           11867   \n",
       "133821167  43004406      38346818        4631037          288783   \n",
       "133821168  45410186      40509200        7627046         1151943   \n",
       "133821169  39837068      35471058        6610718         1008990   \n",
       "133821170  45410187      40509201        7773604         5355936   \n",
       "\n",
       "           tw_last_word  tw_llast_word  tw_len  tw_hash0  tw_hash1  \\\n",
       "133821166          5615           8175       5         0         0   \n",
       "133821167           808            902      11         0         0   \n",
       "133821168          2638          18369       8         0         0   \n",
       "133821169         69708          35969       3         0         0   \n",
       "133821170          3030           2103       9   6521258   5953099   \n",
       "\n",
       "           tw_rt_uhash  TE_b_user_id_tweet_type_language_retweet  \\\n",
       "133821166            0                                  0.080944   \n",
       "133821167      3665864                                       NaN   \n",
       "133821168       139857                                  0.144795   \n",
       "133821169       364477                                       NaN   \n",
       "133821170       325751                                       NaN   \n",
       "\n",
       "           TE_tw_first_word_tweet_type_language_retweet  \\\n",
       "133821166                                      0.016900   \n",
       "133821167                                      0.138499   \n",
       "133821168                                           NaN   \n",
       "133821169                                           NaN   \n",
       "133821170                                           NaN   \n",
       "\n",
       "           TE_tw_last_word_tweet_type_language_retweet  \\\n",
       "133821166                                     0.043753   \n",
       "133821167                                     0.095718   \n",
       "133821168                                     0.066836   \n",
       "133821169                                     0.123103   \n",
       "133821170                                     0.097428   \n",
       "\n",
       "           TE_tw_hash0_tweet_type_language_retweet  \\\n",
       "133821166                                 0.065124   \n",
       "133821167                                 0.113657   \n",
       "133821168                                 0.119549   \n",
       "133821169                                 0.268123   \n",
       "133821170                                      NaN   \n",
       "\n",
       "           TE_tw_hash1_tweet_type_language_retweet  \\\n",
       "133821166                                 0.068161   \n",
       "133821167                                 0.110866   \n",
       "133821168                                 0.112607   \n",
       "133821169                                 0.259497   \n",
       "133821170                                      NaN   \n",
       "\n",
       "           TE_tw_rt_uhash_tweet_type_language_retweet  TE_a_user_id_retweet  \\\n",
       "133821166                                    0.065125              0.099340   \n",
       "133821167                                         NaN              0.095021   \n",
       "133821168                                    0.181900              0.085385   \n",
       "133821169                                         NaN              0.147141   \n",
       "133821170                                    0.095021              0.091062   \n",
       "\n",
       "           TE_b_user_id_retweet  TE_tw_hash_retweet  TE_tw_freq_hash_retweet  \\\n",
       "133821166              0.078053                 NaN                      NaN   \n",
       "133821167              0.078053                 NaN                      NaN   \n",
       "133821168              0.306183                 NaN                      NaN   \n",
       "133821169              0.152514                 NaN                      NaN   \n",
       "133821170                   NaN                 NaN                      NaN   \n",
       "\n",
       "           TE_media_tweet_type_language_a_is_verified_b_is_verified_b_follows_a_retweet  \\\n",
       "133821166                                           0.035084                              \n",
       "133821167                                           0.149485                              \n",
       "133821168                                           0.131317                              \n",
       "133821169                                           0.305488                              \n",
       "133821170                                           0.088363                              \n",
       "\n",
       "           TE_a_count_combined_tweet_type_language_retweet  \\\n",
       "133821166                                         0.113329   \n",
       "133821167                                         0.272158   \n",
       "133821168                                         0.097242   \n",
       "133821169                                         0.504703   \n",
       "133821170                                         0.050882   \n",
       "\n",
       "           TE_a_user_fer_count_delta_time_media_language_retweet  \\\n",
       "133821166                                           0.135183       \n",
       "133821167                                           0.135183       \n",
       "133821168                                           0.106691       \n",
       "133821169                                           0.319379       \n",
       "133821170                                           0.048713       \n",
       "\n",
       "           TE_a_user_fing_count_delta_time_media_language_retweet  \\\n",
       "133821166                                           0.102979        \n",
       "133821167                                           0.102979        \n",
       "133821168                                           0.106691        \n",
       "133821169                                           0.253471        \n",
       "133821170                                           0.068570        \n",
       "\n",
       "           TE_a_user_fering_count_delta_time_tweet_type_language_retweet  \\\n",
       "133821166                                           0.113108               \n",
       "133821167                                           0.268963               \n",
       "133821168                                           0.097242               \n",
       "133821169                                           0.500458               \n",
       "133821170                                           0.032003               \n",
       "\n",
       "           TE_a_user_fing_count_mode_media_language_retweet  \\\n",
       "133821166                                          0.097863   \n",
       "133821167                                          0.097863   \n",
       "133821168                                          0.106691   \n",
       "133821169                                          0.249619   \n",
       "133821170                                          0.070561   \n",
       "\n",
       "           TE_a_user_fer_count_mode_media_language_retweet  \\\n",
       "133821166                                         0.128083   \n",
       "133821167                                         0.128083   \n",
       "133821168                                         0.106691   \n",
       "133821169                                         0.311038   \n",
       "133821170                                         0.062146   \n",
       "\n",
       "           TE_a_user_fering_count_mode_tweet_type_language_retweet  \\\n",
       "133821166                                           0.109425         \n",
       "133821167                                           0.249861         \n",
       "133821168                                           0.097242         \n",
       "133821169                                           0.478840         \n",
       "133821170                                           0.034965         \n",
       "\n",
       "           TE_domains_media_tweet_type_language_retweet  \\\n",
       "133821166                                      0.036662   \n",
       "133821167                                      0.119008   \n",
       "133821168                                      0.116451   \n",
       "133821169                                      0.243152   \n",
       "133821170                                      0.095762   \n",
       "\n",
       "           TE_links_media_tweet_type_language_retweet  \\\n",
       "133821166                                    0.036662   \n",
       "133821167                                    0.119008   \n",
       "133821168                                    0.116451   \n",
       "133821169                                    0.243152   \n",
       "133821170                                    0.095762   \n",
       "\n",
       "           TE_hashtags_media_tweet_type_language_retweet  \n",
       "133821166                                       0.042429  \n",
       "133821167                                       0.050825  \n",
       "133821168                                       0.115911  \n",
       "133821169                                       0.084057  \n",
       "133821170                                            NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(data=test1.drop(RMV, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434838"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-1.xgb' )\n",
    "ytest = model.predict(dtest)\n",
    "del model; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12434838"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = joblib.load( 'model-'+TARGET+'-2.xgb' )\n",
    "ytest += model.predict(dtest)\n",
    "ytest /= 2.\n",
    "del model, dtest; gc.collect()\n",
    "len(ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dt_day\n",
      "13    0.102074\n",
      "14    0.102373\n",
      "15    0.100582\n",
      "16    0.100219\n",
      "17    0.107195\n",
      "18    0.139616\n",
      "19    0.093376\n",
      "Name: ypred, dtype: float32\n",
      "0.096610755 0.13723531 0.12949888 0.097543165 0.70397884 0.7532356 0.7286071944218901\n"
     ]
    }
   ],
   "source": [
    "test1['ypred'] = ytest\n",
    "\n",
    "print( test1.groupby('dt_day')['ypred'].mean() )\n",
    "\n",
    "m17 = test1.loc[ (test1.dt_day==17)&(test1.dt_hour==23), 'ypred' ].mean()\n",
    "m18a = test1.loc[ (test1.dt_day==18)&(test1.dt_hour==0), 'ypred' ].mean()\n",
    "m18b = test1.loc[ (test1.dt_day==18)&(test1.dt_hour==23), 'ypred' ].mean()\n",
    "m19 = test1.loc[ (test1.dt_day==19)&(test1.dt_hour==0), 'ypred' ].mean()\n",
    "print( m17, m18a, m18b, m19, m17/m18a, m19/m18b, 0.5*m17/m18a+ 0.5*m19/m18b )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.101237535"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1.loc[ test1.dt_day==18 ,'ypred'] *= ( 0.5*m17/m18a+ 0.5*m19/m18b )\n",
    "\n",
    "sub['prediction'] = test1['ypred'].values\n",
    "sub.to_csv('submissions/xgb-'+TARGET+'-private-1.csv',header=None, index=False)\n",
    "sub['prediction'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4302e4ceb8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt0 = test0.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt1 = test1.groupby( ['dt_day','dt_hour'] )['ypred'].agg('mean').reset_index()\n",
    "dt0.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "dt1.sort_values(['dt_day','dt_hour'], inplace=True)\n",
    "\n",
    "dt0['ypred2'] = dt1['ypred'].values\n",
    "\n",
    "dt0[['ypred','ypred2']].plot(  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed Time is 45.681281 minutes\n"
     ]
    }
   ],
   "source": [
    "print('Elapsed Time is %f minutes'%((time.time()-startNB)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
